根DNS数据可视化分析

Eric Krokos, Alexander Rowden, K. Whitley, A. Varshney
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引用次数: 17

摘要

分析大量网络数据以监控和保护互联网的核心支柱——根DNS,是一项巨大的挑战。以直观的方式了解根DNS接收的查询的分布,以及这些查询如何随时间变化。传统的查询分析是逐包执行的,缺乏全局、时间和视觉一致性,模糊了潜在的趋势和聚类。我们的方法利用2D和3D渲染技术的模式识别和深度学习的计算能力,快速轻松地解释和与大量根DNS网络流量交互。与现实世界的DNS专家合作,我们的可视化揭示了几个令人惊讶的潜在查询集群,可能是恶意的和良性的,发现了现实世界根DNS DDOS攻击以前未知的特征,并揭示了随着时间的推移,收到的查询分布中不可预见的变化。这些发现将使DNS分析人员更深入地了解其负责的DNS流量的性质,这将有助于他们保护根DNS免受未来的攻击。
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Visual Analytics for Root DNS Data
The analysis of vast amounts of network data for monitoring and safeguarding a core pillar of the internet, the root DNS, is an enormous challenge. Understanding the distribution of the queries received by the root DNS, and how those queries change over time, in an intuitive manner is sought. Traditional query analysis is performed packet by packet, lacking global, temporal, and visual coherence, obscuring latent trends and clusters. Our approach leverages the pattern recognition and computational power of deep learning with 2D and 3D rendering techniques for quick and easy interpretation and interaction with vast amount of root DNS network traffic. Working with real-world DNS experts, our visualization reveals several surprising latent clusters of queries, potentially malicious and benign, discovers previously unknown characteristics of a real-world root DNS DDOS attack, and uncovers unforeseen changes in the distribution of queries received over time. These discoveries will provide DNS analysts with a deeper understanding of the nature of the DNS traffic under their charge, which will help them safeguard the root DNS against future attack.
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